I'm Gopi Krishna Tummala. I bridge the gap between Research Papers and Production Systems. Here is my blueprint for modern AI engineering.
Structured learning paths organized by domain—from generative AI and production systems to autonomous vehicles and agentic intelligence.
From Transformers to Diffusion Models. Understanding the architectures and algorithms powering modern generative AI.
Scaling, serving, and optimizing AI systems. Custom kernels, inference engines, and production infrastructure.
How self-driving cars actually work. Prediction, calibration, sensing, and closed-loop reasoning.
From ReAct loops to multi-agent systems. Building intelligent agents that reason, plan, and act autonomously.
A quick tour through the roles, research labs, and collaborations that shaped my path in AI and autonomous systems.
Adobe · Creative Cloud & Firefly · San Jose, CA
Leading large-scale data pipelines, training infrastructure, and responsible generative AI initiatives that power Firefly and Creative Cloud surfaces.
Autonomous Vehicle Systems · Bay Area, CA
Shipped multi-agent prediction models for L3/L4/L5 autonomous vehicle fleets and co-designed the training framework and dataloaders that kept the stack fed with fresh data.
Qualcomm Research · San Diego, CA
Led prediction for Qualcomm's L3 highway autonomous driving stack—owning forecasting models, simulation harnesses, and post-drive analytics. Earlier built integration and test automation for the stack.
Ph.D. Computer Science & Engineering
Dissertation on collaborative perception and behavior prediction for intelligent transportation systems.
Microsoft Research · Bangalore, India
Designed AutoCalib—large-scale traffic camera calibration with <10% speed error—in Microsoft's video analytics platform.
The Ohio State University · Columbus, OH
Built SmartDashCam, Soft-Swipe, RoadView, and RoadMap; taught introductory programming; collaborated with Honda on live calibration and lane-level localization.
Standard Chartered Bank · Chennai, India
Developed reporting systems and automation scripts for global private banking infrastructure.
Tata Elxsi · Chennai, India
Optimized LTE PDCCH blind decoding algorithms and explored DSP-based radio prototyping.
Indian Institute of Technology Madras · Chennai, India
Graduated with honors; led hostel council committees.
A curated selection of recent publications and projects that explore robust perception, generative modeling, and multi-agent systems at scale.
A modern, industry-standard approach to building robust RAG systems using OpenSearch as the core engine. Transition from simple vector retrieval to production-grade multimodal systems handling text, images, and video with advanced patterns like hybrid search, query rewriting, parent-document retrieval, and cross-encoder reranking.
The journey from stateless inference to stateful, tool-augmented AI agents requires a complete reimagining of MLOps infrastructure. Learn how the Model Context Protocol (MCP), secure sandbox environments, distributed tracing, and holistic versioning enable the next generation of agentic AI systems.
Standard MLOps advice tells you to learn Git and Docker. But for the next generation of AI Engineers, that's just the baseline. This roadmap focuses on the Infrastructure Round—deep-diving into how data is structured for speed, how it's fed into models, how those models scale across clusters, and how we squeeze every drop of performance out of the silicon.
The unsung hero of modern data processing is how we structure data itself. Learn how Apache Parquet and Apache Arrow solve the fundamental trade-off between storage efficiency and compute speed in large-scale analytics and ML pipelines.
A narrative-first walkthrough of reinforcement learning, starting with everyday intuition and ending with the math behind Q-learning and DQN.
Why modern AI teams are handcrafting GPU kernels—from FlashAttention to TPU Pallas code—and how smarter tooling is making silicon-level tuning accessible.
A high level view on how modern vision-language models connect pixels and prose, from CLIP and BLIP to Flamingo, MiniGPT-4, Kosmos, and Gemini.
How PagedAttention, Continuous Batching, Speculative Decoding, and Quantization unlock lightning-fast, reliable large language model serving.
A clear introduction to diffusion and guided diffusion — how a simple physical process became a foundation for modern generative AI, from Stable Diffusion to robotics and protein design.
A reader-friendly guide to scaling AI models beyond the data pipeline—from training loops and distributed frameworks to checkpoints, mixed precision, and fault tolerance.
A deep dive into XGBoost — how second-order Taylor approximations and sophisticated regularization make it the dominant algorithm for structured data, bridging mathematical rigor with system engineering excellence.
How diffusion models predict action sequences instead of pixels. Covers Diffusion Policy, world models for robotics, and connecting diffusion to reinforcement learning for autonomous systems.
The evolution of image diffusion models from U-Net architectures to Diffusion Transformers (DiT). Covers latent diffusion, the DiT revolution, and the complete image generation pipeline.
Deep dive into state-of-the-art video generation models: Sora, Veo 3, and Open-Sora. Plus motion modeling techniques using optical flow, geometry, and diffusion fields.
How video diffusion models are built through pre-training and aligned through post-training. Covers the billion-frame training problem, DPO, RLHF, and the complete training pipeline.
How to accelerate diffusion sampling and control output quality. Covers DDIM, DPM-Solver, Classifier-Free Guidance (CFG), negative prompting, and inference optimization techniques.
Why video is harder than images, the DiT revolution for video, and how diffusion models learn temporal consistency. Covers V-DiT, AsymmDiT, and the mathematical foundations of video generation.
A deep dive into how datasets and dataloaders power modern AI—from the quiet pipeline that feeds models to the sophisticated tools that make training efficient. Understanding the hidden engine that keeps AI systems running.
Why L5 autonomy is harder than a moon landing. Understanding ODD, latency loops, compute constraints, and the probability of failure in autonomous systems.
From photons to decisions: How machines reconstruct 3D reality from 2D data. Covers cameras, IPM, radar, LiDAR, and sensor fusion in an intuitive, first-principles approach.
If you don't know where your eyes are relative to your feet, you trip. Covers intrinsics, extrinsics, SE(3) transforms, online vs. offline calibration, and time synchronization.
From GPS to centimeter accuracy: How autonomous vehicles know their exact position. Covers GNSS, IMU, wheel odometry, scan matching, and the Kalman Filter fusion that creates the "Blue Line."
From perception to action: How autonomous vehicles make decisions. Covers cost functions, game-theoretic planning, and the modular vs. end-to-end debate.
The hardest problem in AV: predicting human irrationality. Covers the evolution from physics-based prediction to Generative AI, tracking the journey through Waymo Open Dataset Challenges.
A deep dive into physics-aware video diffusion models: how researchers inject physical constraints into generative models, the three leading technical approaches, and their practical impact on robotics and scientific simulation.
An intuitive introduction to the Transformer architecture — from the attention mechanism to self-attention and cross-attention, using language translation as a concrete example.
An intuitive introduction to Variational Autoencoders — how compressing data into probabilistic codes enables machines to generate realistic images, sounds, and structures.
Part 4 of a comprehensive guide to agentic AI design patterns. Covers common failure modes, safety mechanisms, verifiable pipelines, and how to build reliable production systems.
Part 3 of a comprehensive guide to agentic AI design patterns. Covers specialized patterns: embodied agents, 3D scene understanding, imagination loops, multi-agent societies, error recovery, and self-debugging.
Part 5 of a comprehensive guide to agentic AI design patterns. Covers 2025 trends, cost optimization, case studies, production checklist, and the state of the field.
Part 1 of a comprehensive guide to agentic AI design patterns. Covers the fundamentals: ReAct loops, planning, tool use, self-consistency, and graph-based reasoning.
Part 2 of a comprehensive guide to agentic AI design patterns. Covers production-ready patterns: memory management, supervisor/orchestrator, parallel tool execution, and hidden reasoning.
An exploration of modern agent systems, with math, analogies, and examples. From ReAct loops to multi-agent societies, discover the design patterns that make AI agents think, act, and fix themselves.
Reflections on building production-grade behavior prediction systems for autonomous vehicles — and why closed-loop reasoning is the bridge between perception and planning.
How we used deep learning to automatically calibrate traffic cameras by observing vehicle motion—work that won Best Paper Award at ACM BuildSys 2017.
My research journey from wireless communication foundations to solving the camera calibration bottleneck that enables autonomous vehicle vision.
A structured articulation and pacing warm-up designed to help technologists speak with clarity and confidence in high-stakes meetings.
A collaborative 45-minute thinking algorithm tuned for Google-style coding interviews—classify the problem, co-design an optimal approach, code with confidence, and handle follow-ups with ease.
A curated list of book recommendations covering personal development, philosophy, psychology, and life lessons from my personal library.